• A company at a higher stage of the data maturity model can integrate data analysis into business practices.
  • Beyond maximizing financial results for the firm, the ability of accelerators to minimize waste has a significant social, environmental, and cultural impact.

Have you ever wondered what fuels the digital world? You got it right; it’s data! But how do companies hunt for new data?

Organizations are generating more data in new and different ways, including the proliferation of edge and IoT devices through new app monitoring and observability streams and the increasing data generated from new digital services and experiences.

Firms are mandated to leverage their wealth of data that can benefit their business than allowing it to become an untapped resource.

But how can organizations build mature data innovation practices that can help them unlock the value of data? The multi-faceted process calls for a comprehensive look at high-quality data and technologies that can help scale and keep pace with vast and accelerating data generation.

Additionally, it requires cross-functional collaboration between technology teams like data scientists, developers, and analytics teams to support the organization’s “data disruptors” effectively.

Segmenting the Market in Terms of Data Innovation Maturity

Some of the important questions that can help gauge the maturity of the data are:

  • How thoroughly they integrate their data.
  • How well their statistics are gathered (i.e., their accuracy and quality).
  • The effectiveness of IT and data users’ cooperation throughout project lifecycles.
  • The use of technologies like machine learning and artificial intelligence to support, scale, and automate data management processes.

Typically, there are three stages of data innovation maturity. Understanding the data maturity stages allows companies to execute the development of data usage. Here are three common stages of a data maturity model:

Level 1: This is the initial stage where a company is in the process of putting a data strategy in place. Rather than sourcing data from online and external sources, it relies on internal data – from surveys and other data measuring tools.

Most enterprises at this stage struggle with a fragmented view of their imperfect data and are also prone to data loss, hacks, and major disruptions. Neither is there any data classification or data backup. To mount it all, there’s no action plan for disaster recovery and data restoration in case of major data loss.

Level 2: This is the stage where organizations get a clear understanding of the importance of data and how it can enhance business operations. At this point, enterprises are in the process of adopting data into their decision-making process. Results of the business and progress are analyzed by employees using data. Because this calls for increased use of data, appropriate security measures and automation processes are in place to maintain data flow.

Level 3: This is the advanced stage of data maturity that calls for analyzing data usage from earlier stages and using the knowledge derived to compete in the marketplace. Companies at this stage have a user-friendly process to access data and promote data literacy.

Most organizations fall into Level 1, with fragmented and inefficient data management practices. With a fragmented view of their imperfect data, most organizations struggle and lack the technologies to keep pace with their data and are grappling with infighting among teams. At the same time, just 16% of organizations meet the threshold of operating an Accelerated Data Innovation practice. It is imperative that organizations radically evolve how they approach data management today to maximize the return on their data capital.

How to use data maturity models?

Professionals use data maturity models to determine their company’s data management practices. This helps them set goals and how their company can advance to the next level. A company at a higher stage of the data maturity model can integrate data analysis into business practices. This results in effective and data-driven choices.

Quantifying the Impact of Accelerated Data Innovation

Effective data management and analytics lead to better insights and positively impacts revenue. The benefits are, in fact, multi-faceted. Organizations operating in this environment have more data at their disposal. Organizations also witness positive changes in their data management practices, such as improved product or service quality, enhanced IT/application availability/predictability, superior customer service/experience, increased employee efficiency/productivity, and reduced organizational risk. Let’s take a look at some of the benefits:

Fuels decision-making: With data maturity models, organizations clearly understand how their employees are incorporating data into the decision-making process. Professionals can make informed decisions with data. For instance, when professionals at the management level start interpreting data, the information derived can be used to support the top performers in their company and guide the low performers toward better productivity.

Customer satisfaction: With their ability to interpret, understand, and react to market demands, data innovation accelerators fuel innovation. Organizations can maximize customer experience. Today’s organizations are focusing heavily on customer outcomes. Efficient data management practices that support innovation, differentiation, and higher customer satisfaction can significantly contribute toward this end.

Increased productivity:  Yet another way by which data management practice can drive value for an organization is by providing improved insight into employee productivity. Organizations can use the data from employees’ everyday work to identify loopholes that impact productivity and engagement. Employees, too, benefit as they can get key insights into key areas of improvement.

Deliver innovative offerings: The insights offered by data can be used for the purpose of innovation. Better data analysis can help organizations uncover opportunities within the market and help them gauge and adjust to changing customer preferences. This will help them cut competition and fulfil needs in real-time.

Maximizes app availability: With organizations using modern applications and increasing the use of microservices, containers, and Kubernetes, the amount and types of data produced by applications, too, changes. This can result in application monitoring and observability challenges. Organizations must increasingly apply advanced data management and analytics capabilities to effectively measure performance, availability, and user experience.


It’s become a necessity for almost every organization to use data for digital transformation. Besides, they also need to manage the data well so as to drive differentiation to drive measurable value. Unlocking the true value can deliver results and empower their journey towards innovation.

Data Innovation Maturity provides an edge. Firms having high data innovation maturity are confident that they have the technical expertise required to adapt and thrive through disruptions. With the business climate becoming more challenging, businesses must truly identify the potential of data that that can make a true difference.